What changes in maxillary morphology from distraction osteogenesis maxillary expansion (DOME) correlate with subjective and objective OSA measures?

OBJECTIVES: To correlate skeletal and airway measures on imaging with polysomnographic and self-reported measures after distraction osteogenesis maxillary expansion (DOME), in the effort to identify clinically relevant sites of expansion to guide treatment for adult patients with obstructive sleep apnea (OSA). MATERIALS AND METHODS: This is a retrospective study reviewing subjects who underwent DOME and had the complete set of the following data: peri-treatment cone-beam computed tomography (CBCT) scans, polysomnography (PSG), Epworth Sleepiness Scale (ESS), and nasal obstruction symptom (NOSE) scores. RESULTS: Of 132 subjects who underwent DOME, 35 met inclusion criteria (71% men, mean age 27.7 ± 6.5 years, mean BMI 26.0 ± 6.4 kg/m2) and were enrolled in the study. There was a significant reduction in the NOSE score from 11.4 ± 5.5 to 3.6 ± 3.1, in the ESS score from 12.0 ± 4.6 to 7.1 ± 4.7, and in the apnea-hypopnea index (AHI) from 17.1 ± 15.8 to 7.01 ± 6.2 (p < 0.0001), after DOME. Nasal floor width at the nasopalatine canal level showed a statistically significant correlation with AHI reduction (p < .0001). CONCLUSIONS: DOME is significantly associated with reduction of nasal obstruction, sleepiness, and severity of OSA. The findings suggest that expansion at the anterior third of the bony nasal passage, specifically where the nasopalatine canal is located predicts its clinical efficacy. This site may be a useful target anatomically via imaging.

Predicting soft tissue changes after orthognathic surgery: The sparse partial least squares method.

OBJECTIVES: To develop a prediction algorithm for soft tissue changes after orthognathic surgery that would result in accurate predictions (1) regardless of types or complexity of operations and (2) with a minimum number of input variables. MATERIALS AND METHODS: The subjects consisted of 318 patients who had undergone the surgical correction of Class II or Class III malocclusions. Two multivariate methods-the partial least squares (PLS) and the sparse partial least squares (SPLS) methods-were used to construct prediction equations. While the PLS prediction model included 232 input variables, the SPLS method included a reduced number of variables generated by a handicapping algorithm via the sparsity control. The accuracy between the PLS and SPLS models was compared. RESULTS: There were no significant differences in prediction accuracy depending on surgical movements, the sex of the subjects, or additional surgeries. The predictive performance with a reduced set of 34 input variables chosen using the SPLS method was statistically indistinguishable from the full set of variables with the original PLS prediction model. CONCLUSIONS: The prediction method proposed in the present study was accurate for a wide range of orthognathic surgeries. A reduced set of input variables could be selected through the SPLS method while simultaneously maintaining a prediction level that was as accurate as that of the original PLS prediction model.

Use of mini-implants to avoid maxillary surgery for Class III mandibular prognathic patient: a long-term post-retention case.

Because of the potential morbidity and complications associated with surgical procedures, limiting the extent of orthognathic surgery is a desire for many orthodontic patients. An eighteen-year-old woman had a severe Class III malocclusion and required bi-maxillary surgery. By changing the patient's maxillary occlusal plane using orthodontic mini-implants, she was able to avoid the maxillary surgery; requiring only a mandibular setback surgery. To accurately predict the post-surgery outcome, we applied a new soft tissue prediction method. We were able to follow and report the long-term result of her combined orthodontic and orthognathic treatment. The changes to her occlusal plane continue to appear stable over 6 years later.

A more accurate soft-tissue prediction model for Class III 2-jaw surgeries.

INTRODUCTION: The use of bimaxillary surgeries to treat Class III malocclusions makes the results of the surgeries more complicated to estimate accurately. Therefore, our objective was to develop an accurate soft-tissue prediction model that can be universally applied to Class III surgical-orthodontic patients regardless of the type of surgical correction: maxillary or mandibular surgery with or without genioplasty. METHODS: The subjects of this study consisted of 204 mandibular setback patients who had undergone the combined surgical-orthodontic correction of severe skeletal Class III malocclusions. Among them, 133 patients had maxillary surgeries, and 81 patients received genioplasties. The prediction model included 226 independent and 64 dependent variables. Two prediction methods, the conventional ordinary least squares method and the partial least squares (PLS) method, were compared. When evaluating the prediction methods, the actual surgical outcome was the gold standard. After fitting the equations, test errors were calculated in absolute values and root mean square values through the leave-1-out cross-validation method. RESULTS: The validation result demonstrated that the multivariate PLS prediction model with 30 orthogonal components showed the best prediction quality among others. With the PLS method, the pattern of prediction errors between 1-jaw and 2-jaw surgeries did not show a significantly difference. CONCLUSIONS: The multivariate PLS prediction model based on about 30 latent variables might provide an improved algorithm in predicting surgical outcomes after 1-jaw and 2-jaw surgical corrections for Class III patients.

Extraction sockets: erratic healing impeding factors.

AIM: The aim of this exploratory study was to analyse prevalence of extraction sockets showing erratic healing and evaluate factors potentially impeding healing. METHODS: Erratic healing was defined as extraction sites showing clinical observations of fibrous scar tissue occupying the extraction site rather than bone following 12 or more weeks of healing. Computerized tomography was used to evaluate characteristics and calculate Hounsfield unit scores for sites showing erratic healing. RESULTS: A total of 1226 dental records from Seoul National University Dental Hospital archives including patients subject to extractions prior to implant placement were evaluated. Seventy subjects (5.71%) and 97 sites (4.24%) exhibited erratic extraction socket healing. Maxillary incisor/canine sites showed the lowest (0.47%), whereas mandibular molar sites the highest (5.41%) occurrence. In the multivariable analysis, erratic healing was more likely to occur in subjects <60 years old (OR = 2.23, 95%CI = 1.26-3.94), subjects with hypertension (OR = 2.37, 95%CI = 1.24-4.55), in molar sites (OR = 4.91, 95%CI = 1.41-17.07), and following single tooth extractions (OR = 2.98, 95%CI = 1.36-6.53). Computerized tomography showed the highest incidence of bone loss for the buccal wall (49.3%). CONCLUSION: Erratic extraction socket healing appears a not uncommon sequel and local factors seem to be major contributors to its occurrence.

A better statistical method of predicting postsurgery soft tissue response in Class II patients.

OBJECTIVE: To propose a better statistical method of predicting postsurgery soft tissue response in Class II patients. MATERIALS AND METHODS: The subjects comprise 80 patients who had undergone surgical correction of severe Class II malocclusions. Using 228 predictor and 64 soft tissue response variables, we applied two multivariate methods of forming prediction equations, the conventional ordinary least squares (OLS) method and the partial least squares (PLS) method. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a leave-one-out cross-validation method was used. RESULTS: The multivariate PLS method provided a significantly more accurate prediction than the conventional OLS method. CONCLUSION: The multivariate PLS method was more satisfactory than the OLS method in accurately predicting the soft tissue profile change after surgical correction of severe Class II malocclusions.

A more accurate method of predicting soft tissue changes after mandibular setback surgery.

PURPOSE: To propose a more accurate method to predict the soft tissue changes after orthognathic surgery. PATIENTS AND METHODS: The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordinary least-squares (OLS) and the partial least-squares (PLS) methods. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a 10-fold cross-validation method was used. RESULTS: The multivariate PLS method showed significantly better predictive performance than the conventional OLS method. The bias pattern was more favorable and the absolute prediction accuracy was significantly better with the PLS method than with the OLS method. CONCLUSIONS: The multivariate PLS method was more satisfactory than the conventional OLS method in accurately predicting the soft tissue profile change after Class III mandibular setback surgery.